On Continuous Queries in Stream Processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Stream processing is about processing continuous streams of data by programs in a workflow. Continuous execution is discretized by grouping input stream tuples into batches and using one batch at a time for the execution of programs. The programs may generate stream data which may be input to subsequent programs in the workflow. They may also read as well as write some data in persistent store. Continuous queries are processed in the workflow. A continuous query (CQ) consists of a sequence of one time queries. There is a general agreement that each CQ normally spans over several batches of stream inputs. Apart from this, different notions of CQs exist in the literature, their one time queries ranging from just transactions (that is, single executions of programs) to composite transactions. In this paper, we look at how CQs can be defined generically and propose a correctness criterion for concurrent executions of CQs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it